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Published in:

2021 | OriginalPaper | Chapter

# Understanding Energy Use in Indian Agricultural Production System in Post-WTO Period

Authors: Manoj Bhatt, Surya Bhushan

Published in:

Publisher: Springer Singapore

## Abstract

This paper tries to explore the intensity of energy usage in agriculture sector in various states of India. Further panel data approach has been put to use to understand the energy use in post-WTO period. The picture sketched by this investigation demonstrates that spatial and temporal distribution of agricultural productivities varies markedly among states. Further, the results indicate that the high productive states saw a negative response of animal labour and chemical fertilizer use in the food grain production.
Footnotes
1
There exists a sizable body of the literature on the politics of economic reforms in India initiated in the early 1990s (Bardhan, 1999, 2001, 2003, 2005; Jenkins, 1999; Varshney, 1999; Corbridge & Harriss, 2000; Mooij 2005).

2
Bhalla and Singh (2010) observed a declining trend in interstate disparity in the use of modern inputs over 1962–65 to 2003-06. The coefficient of variations among states declined from 398 to 152 for tractors used, from 733 to 62 for number of tube wells, from 531 to 118 for fertilizer consumption, and from 251 to 88 for irrigation intensity during these periods. They further raised the question of long-run sustainability to maintain the agricultural growth through increasingly higher use of costly and heavily subsidized inputs that not only lead to soil and environmental degradation but also putting pressure on political economy.

3
For details on econometric methodology, see Baltagi (2013, Chap. 2) and Wooldridge (2013, Chap. 14) for good overviews of fixed-effect and random-effect models. STATA has been used to estimate this model. There is considerable debate regarding the choice between the FE and RE models (Griliches, 1984).

4
The Hausman test helps in detecting violation of the random-effect modelling assumption that the explanatory variables are orthogonal to the unit effects. In case of no correlation between the independent variable(s) and the unit effects, then estimates of β in the fixed-effect model $$\left( {\hat{\beta }_{FE} } \right)$$ should be similar to estimates of β in the random-effect model $$\left( {\hat{\beta }_{RE} } \right)$$ (for instance, as in Haspolat, 2015; Clark & Linzer, 2015). The Hausman test statistic H is a measure of the difference between the two estimates:
$$H = \left( {\hat{\beta }_{RE} - \hat{\beta }_{FE} } \right)^{{\prime }} \left[ {Var\left( {\hat{\beta }_{FE} } \right) - Var\left( {\hat{\beta }_{RE} } \right)} \right]^{ - 1} \left( {\hat{\beta }_{RE} - \hat{\beta }_{FE} } \right)$$
Under the null hypothesis of orthogonality, H is distributed chi-square with degrees of freedom equal to the number of regressors in the model. A finding that p < 0:05 is taken as evidence that the two models are different enough to reject the null hypothesis, and hence to reject the random-effect model in favour of the fixed-effect model.

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